This paper develops a compact and portable X-band vector network analyzer (VNA) for the low-cost microwave image sensing applications. Since significant local oscillator (LO) injection pulling and poor LO phase noise performance may reduce the accuracy of measurement, the developed VNA uses the single-conversion technique and the phase noise cancellation technique to suppress the injection pulling effect and the LO phase noise, respectively. A hybrid phase detection algorithm that includes arctangent, arcsine, and arccosine functions is proposed to overcome the bottleneck of the distortion of conventional quadrature phase detection in some phase regions. The experimental results demonstrate that the X-band microwave image sensing system that uses the developed VNA, which is highly compact and portable, can effectively yield a contour image of bones in a chicken wing and pork ribs.

Neural style transfer (NST) is a technique based on the deep learning of a convolutional neural network (CNN) to create entertaining pictures by cleverly stylizing ordinary pictures with the predetermined visual art style. However, three issues must be carefully investigated during the generation of neural-stylized artwork: the color scheme, the strength of style of the strokes, and the adjustment of image contrast. To solve these problems and select image colorization based on personal preference, in this paper, we propose modified universal-style transfer (UST) method combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the three above-mentioned issues simultaneously. This work provides more visual effects for stylized images, and also can integrate into the UST method effectively. In addition, the proposed method is suitable for stylized images generated by any NST method, but it also works similarly to the Multi-Style Transfer (MST) method, which mixes two different stylized images. Finally, our proposed method successfully combined the modified UST method and post-processing method to meet personal preference.

In the heat treatment of steel, decarburization is a serious issue that leads to poor wear resistance and low fatigue life. At present, the decarburization depth was determined using a visual estimation by the human eye, and the software estimation was determined through traditional image analysis. Therefore, decarburization depth analysis remains limited in experts and traditional algorithms. Artificial intelligence is a general-purpose technology that has a multitude of applications. This paper uses the concept of deep learning to propose a decarburization layer detector (DLD) that can determine the depth of decarburized layers. This DLD system boasts high performance, real-time, low learning, and computation costs. In addition, we used several kinds of decarburized layers images to compare the proposed method with other deep learning network architectures. The experimental results show that the proposed method yields a detection accuracy of 92.97%, which is higher than existing methods and boasts computational demands which are far lower than other network architectures. Therefore, we propose a novel system for automatic decarburization depth determination as an application for metallographic analysis.